In the past few years, many great person search frameworks have been proposed. Most of the existing network are designed to learn more accurate bounding boxes [1]}, more discriminative re-ID features [2]}, or reconcile the relationship between detection and re-ID [3]}, [4]}. Prior researches tried to transform the person search problem into solving the person detection and re-ID, and the difference is to tackle them separately or jointly. From two-step methods to one-step methods, the re-ID branch generally relies on the detection results, and only employs the information within the bounding box to learn re-ID features. However, the person search images naturally contains much richer information, including the labeled persons, unlabeled persons and complete background, which could provide complementary information for re-ID learning. Inspired by this, some prior works have been proposed to explore the role of context information in person search [5]}, [6]} in an implicit and weakly supervised way. Group context has also been explored in several works by graph matching [7]}, [1]}, [9]}, where the context information is applied in a pair-wise way. These pair-based works firstly extract feature of all persons existing in an image pair, then optimize the matching the two feature sets in a graph level, instead of making the matching one by one in a greedy style. Therefore, we propose a unified global-local context network (GLCNet) to explicitly address these issues.
